

import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor







def       lowercase					(	__A :					Union[str, Any]		)				->			Any:





    '''simple docstring'''




    if "cls_token" in name:
        snake_case							:		List[str]        =		name.replace("""cls_token""" ,    """vit.embeddings.cls_token"""		)
    if "mask_token" in name:
        snake_case							:		Dict        =		name.replace("""mask_token""" ,    """decoder.mask_token"""		)
    if "decoder_pos_embed" in name:
        snake_case							:		List[str]        =		name.replace("""decoder_pos_embed""" ,    """decoder.decoder_pos_embed"""		)
    if "pos_embed" in name and "decoder" not in name:
        snake_case							:		Optional[Any]        =		name.replace("""pos_embed""" ,    """vit.embeddings.position_embeddings"""		)
    if "patch_embed.proj" in name:
        snake_case							:		Any        =		name.replace("""patch_embed.proj""" ,    """vit.embeddings.patch_embeddings.projection"""		)
    if "patch_embed.norm" in name:
        snake_case							:		Optional[int]        =		name.replace("""patch_embed.norm""" ,    """vit.embeddings.norm"""		)
    if "decoder_blocks" in name:
        snake_case							:		Optional[Any]        =		name.replace("""decoder_blocks""" ,    """decoder.decoder_layers"""		)
    if "blocks" in name:
        snake_case							:		int        =		name.replace("""blocks""" ,    """vit.encoder.layer"""		)
    if "attn.proj" in name:
        snake_case							:		Tuple        =		name.replace("""attn.proj""" ,    """attention.output.dense"""		)
    if "attn" in name:
        snake_case							:		Tuple        =		name.replace("""attn""" ,    """attention.self"""		)
    if "norm1" in name:
        snake_case							:		Tuple        =		name.replace("""norm1""" ,    """layernorm_before"""		)
    if "norm2" in name:
        snake_case							:		List[str]        =		name.replace("""norm2""" ,    """layernorm_after"""		)
    if "mlp.fc1" in name:
        snake_case							:		Dict        =		name.replace("""mlp.fc1""" ,    """intermediate.dense"""		)
    if "mlp.fc2" in name:
        snake_case							:		Dict        =		name.replace("""mlp.fc2""" ,    """output.dense"""		)
    if "decoder_embed" in name:
        snake_case							:		Union[str, Any]        =		name.replace("""decoder_embed""" ,    """decoder.decoder_embed"""		)
    if "decoder_norm" in name:
        snake_case							:		Tuple        =		name.replace("""decoder_norm""" ,    """decoder.decoder_norm"""		)
    if "decoder_pred" in name:
        snake_case							:		Optional[Any]        =		name.replace("""decoder_pred""" ,    """decoder.decoder_pred"""		)
    if "norm.weight" in name and "decoder" not in name:
        snake_case							:		List[Any]        =		name.replace("""norm.weight""" ,    """vit.layernorm.weight"""		)
    if "norm.bias" in name and "decoder" not in name:
        snake_case							:		Optional[Any]        =		name.replace("""norm.bias""" ,    """vit.layernorm.bias"""		)

    return name







def       lowercase					(	__A :					Tuple ,    __A :					Optional[int]		)				->			Tuple:





    '''simple docstring'''




    for key in orig_state_dict.copy().keys():
        snake_case							:		Any        =		orig_state_dict.pop(__A		)

        if "qkv" in key:
            snake_case							:		List[Any]        =		key.split("""."""		)
            snake_case							:		int        =		int(key_split[1]		)
            if "decoder_blocks" in key:
                snake_case							:		int        =		config.decoder_hidden_size
                snake_case							:		Union[str, Any]        =		"""decoder.decoder_layers."""
                if "weight" in key:
                    snake_case							:		Optional[Any]        =		val[:dim, :]
                    snake_case							:		Tuple        =		val[dim : dim * 2, :]
                    snake_case							:		Optional[int]        =		val[-dim:, :]
                elif "bias" in key:
                    snake_case							:		Union[str, Any]        =		val[:dim]
                    snake_case							:		int        =		val[dim : dim * 2]
                    snake_case							:		Optional[Any]        =		val[-dim:]
            else:
                snake_case							:		List[str]        =		config.hidden_size
                snake_case							:		List[str]        =		"""vit.encoder.layer."""
                if "weight" in key:
                    snake_case							:		Any        =		val[:dim, :]
                    snake_case							:		int        =		val[dim : dim * 2, :]
                    snake_case							:		Union[str, Any]        =		val[-dim:, :]
                elif "bias" in key:
                    snake_case							:		Optional[Any]        =		val[:dim]
                    snake_case							:		int        =		val[dim : dim * 2]
                    snake_case							:		Optional[int]        =		val[-dim:]

        else:
            snake_case							:		Optional[Any]        =		val

    return orig_state_dict







def       lowercase					(	__A :					Tuple ,    __A :					str		)				->			List[Any]:





    '''simple docstring'''




    snake_case							:		Optional[int]        =		ViTMAEConfig()
    if "large" in checkpoint_url:
        snake_case							:		List[str]        =		1024
        snake_case							:		Optional[int]        =		4096
        snake_case							:		Optional[int]        =		24
        snake_case							:		Tuple        =		16
    elif "huge" in checkpoint_url:
        snake_case							:		Dict        =		14
        snake_case							:		int        =		1280
        snake_case							:		Dict        =		5120
        snake_case							:		List[str]        =		32
        snake_case							:		Optional[Any]        =		16

    snake_case							:		str        =		ViTMAEForPreTraining(__A		)

    snake_case							:		Optional[int]        =		torch.hub.load_state_dict_from_url(__A ,    map_location="""cpu"""		)["""model"""]

    snake_case							:		Any        =		ViTMAEImageProcessor(size=config.image_size		)

    snake_case							:		Tuple        =		convert_state_dict(__A ,    __A		)

    model.load_state_dict(__A		)
    model.eval()

    snake_case							:		Tuple        =		"""https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

    snake_case							:		Union[str, Any]        =		Image.open(requests.get(__A ,    stream=__A		).raw		)
    snake_case							:		Dict        =		ViTMAEImageProcessor(size=config.image_size		)
    snake_case							:		str        =		image_processor(images=__A ,    return_tensors="""pt"""		)

    # forward pass
    torch.manual_seed(2		)
    snake_case							:		List[str]        =		model(**__A		)
    snake_case							:		str        =		outputs.logits

    if "large" in checkpoint_url:
        snake_case							:		str        =		torch.tensor(
            [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]		)
    elif "huge" in checkpoint_url:
        snake_case							:		List[Any]        =		torch.tensor(
            [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]		)
    else:
        snake_case							:		Optional[int]        =		torch.tensor(
            [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]		)

    # verify logits
    assert torch.allclose(logits[0, :3, :3] ,    __A ,    atol=1E-4		)

    print(f"""Saving model to {pytorch_dump_folder_path}"""		)
    model.save_pretrained(__A		)

    print(f"""Saving image processor to {pytorch_dump_folder_path}"""		)
    image_processor.save_pretrained(__A		)


if __name__ == "__main__":
     __lowercase     : Optional[int]       =						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--checkpoint_url''',
         default='''https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth''',
         type=str,
         help='''URL of the checkpoint you\'d like to convert.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
     )

     __lowercase     : Optional[Any]       =						parser.parse_args()
     convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)











"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					KandinskyImgaImgPipeline
    SCREAMING_SNAKE_CASE		:	Tuple		=					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
    SCREAMING_SNAKE_CASE		:	int		=					[
        """prompt""",
        """negative_prompt""",
        """image_embeds""",
        """negative_image_embeds""",
        """image""",
    ]
    SCREAMING_SNAKE_CASE		:	List[str]		=					[
        """generator""",
        """height""",
        """width""",
        """strength""",
        """guidance_scale""",
        """negative_prompt""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    SCREAMING_SNAKE_CASE		:	Any		=					False






    @property
    def   UpperCamelCase			(			self	:				List[str]   )	->						str:
      return 32






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      return 32






    @property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						str:
      return self.time_input_dim






    @property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[Any]:
      return self.time_input_dim * 4






    @property
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      return 100






    @property
    def   UpperCamelCase			(			self	:				Any   )	->						List[Any]:
      lowerCamelCase_       	=		XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
      return tokenizer






    @property
    def   UpperCamelCase			(			self	:				str   )	->						Optional[Any]:
      torch.manual_seed(0   )
      lowerCamelCase_       	=		MCLIPConfig(
          numDims=self.cross_attention_dim			,		transformerDimensions=self.text_embedder_hidden_size			,		hidden_size=self.text_embedder_hidden_size			,		intermediate_size=37			,		num_attention_heads=4			,		num_hidden_layers=5			,		vocab_size=1005			,		)

      lowerCamelCase_       	=		MultilingualCLIP(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		text_encoder.eval()

      return text_encoder






    @property
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      torch.manual_seed(0   )

      lowerCamelCase_       	=		{
          'in_channels': 4,
          # Out channels is double in channels because predicts mean and variance
          'out_channels': 8,
          'addition_embed_type': 'text_image',
          'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
          'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
          'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
          'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
          'layers_per_block': 1,
          'encoder_hid_dim': self.text_embedder_hidden_size,
          'encoder_hid_dim_type': 'text_image_proj',
          'cross_attention_dim': self.cross_attention_dim,
          'attention_head_dim': 4,
          'resnet_time_scale_shift': 'scale_shift',
          'class_embed_type': None,
      }

      lowerCamelCase_       	=		UNetaDConditionModel(**__SCREAMING_SNAKE_CASE   )
      return model






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }






    @property
    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[Any]:
      torch.manual_seed(0   )
      lowerCamelCase_       	=		VQModel(**self.dummy_movq_kwargs   )
      return model






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						List[Any]:
      lowerCamelCase_       	=		self.dummy_text_encoder
      lowerCamelCase_       	=		self.dummy_tokenizer
      lowerCamelCase_       	=		self.dummy_unet
      lowerCamelCase_       	=		self.dummy_movq

      lowerCamelCase_       	=		{
          'num_train_timesteps': 1000,
          'beta_schedule': 'linear',
          'beta_start': 0.00_085,
          'beta_end': 0.012,
          'clip_sample': False,
          'set_alpha_to_one': False,
          'steps_offset': 0,
          'prediction_type': 'epsilon',
          'thresholding': False,
      }

      lowerCamelCase_       	=		DDIMScheduler(**__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		{
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Dict=0   )	->						str:
      lowerCamelCase_       	=		floats_tensor((1, self.cross_attention_dim)			,		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		floats_tensor((1, self.cross_attention_dim)			,		rng=random.Random(seed + 1   )   ).to(__SCREAMING_SNAKE_CASE   )
      # create init_image
      lowerCamelCase_       	=		floats_tensor((1, 3, 64, 64)			,		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		image.cpu().permute(0			,		2			,		3			,		1   )[0]
      lowerCamelCase_       	=		Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert('RGB'   ).resize((256, 256)   )

      if str(__SCREAMING_SNAKE_CASE   ).startswith('mps'   ):
        lowerCamelCase_       	=		torch.manual_seed(__SCREAMING_SNAKE_CASE   )
      else:
        lowerCamelCase_       	=		torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		{
          'prompt': 'horse',
          'image': init_image,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 64,
          'width': 64,
          'num_inference_steps': 10,
          'guidance_scale': 7.0,
          'strength': 0.2,
          'output_type': 'np',
      }
      return inputs






    def   UpperCamelCase			(			self	:				Tuple   )	->						Any:
      lowerCamelCase_       	=		'cpu'

      lowerCamelCase_       	=		self.get_dummy_components()

      lowerCamelCase_       	=		self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		pipe.to(__SCREAMING_SNAKE_CASE   )

      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )   )
      lowerCamelCase_       	=		output.images

      lowerCamelCase_       	=		pipe(
          **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )			,		return_dict=__SCREAMING_SNAKE_CASE			,		)[0]

      lowerCamelCase_       	=		image[0, -3:, -3:, -1]
      lowerCamelCase_       	=		image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      lowerCamelCase_       	=		np.array(
          [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]   )
      assert (
          np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
      ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
      ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class 	a		(   unittest.TestCase						):






    def   UpperCamelCase			(			self	:				Tuple   )	->						List[str]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def   UpperCamelCase			(			self	:				Any   )	->						int:
      lowerCamelCase_       	=		load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinsky/kandinsky_img2img_frog.npy'   )

      lowerCamelCase_       	=		load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
      lowerCamelCase_       	=		'A red cartoon frog, 4k'

      lowerCamelCase_       	=		KandinskyPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-prior'			,		torch_dtype=torch.floataa   )
      pipe_prior.to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		KandinskyImgaImgPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1'			,		torch_dtype=torch.floataa   )
      lowerCamelCase_       	=		pipeline.to(__SCREAMING_SNAKE_CASE   )

      pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.Generator(device='cpu'   ).manual_seed(0   )
      lowerCamelCase_	,      lowerCamelCase_       	=		pipe_prior(
          __SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		num_inference_steps=5			,		negative_prompt=''			,		).to_tuple()

      lowerCamelCase_       	=		pipeline(
          __SCREAMING_SNAKE_CASE			,		image=__SCREAMING_SNAKE_CASE			,		image_embeds=__SCREAMING_SNAKE_CASE			,		negative_image_embeds=__SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		num_inference_steps=100			,		height=768			,		width=768			,		strength=0.2			,		output_type='np'			,		)

      lowerCamelCase_       	=		output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )




